OpenRaiser/Intern-Atlas huggingface dataset:
Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
Venue: arXiv cs.CV 2018
Year: 2018

We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography. The approximate model introduces aliasing artefacts in the gradient information for the iterative reconstruction, but these artefacts are highly structured and we can train a CNN that can use the approximate information to perform an iterative reconstruction. We show feasibility of the method for human in-vivo measurements in a limited-view geometry. The proposed method is able to produce superior results to total variation reconstructions with a speed-up of 32 times.
Design and Evaluation of a Tutor Platform for Personalized Vocabulary Learning
Venue: arXiv cs.AI 2018
Year: 2018

This paper presents our experiences in designing, implementing, and piloting an intelligent vocabulary learning tutor. The design builds on several intelligent tutoring design concepts, including graph-based knowledge representation, learner modeling, and adaptive learning content and assessment exposition. Specifically, we design a novel phased learner model approach to enable systematic exposure to words during vocabulary instruction. We also built an example application over the tutor platform that uses a learning activity involving videos and an assessment activity involving word to picture/image association. More importantly, the tutor adapts to the significant variation in children's knowledge at the beginning of kindergarten, and evolves the application at the speed of each individual learner. A pilot study with 180 kindergarten learners allowed the tutor to collect various kinds of activity information suitable for insights and interventions both at an individual- and class-level. The effort also demonstrates that we can do A/B testing for a variety of hypotheses at scale with such a framework.
Optimization of a SSP's Header Bidding Strategy using Thompson Sampling
Venue: arXiv cs.LG 2018
Year: 2018

Over the last decade, digital media (web or app publishers) generalized the use of real time ad auctions to sell their ad spaces. Multiple auction platforms, also called Supply-Side Platforms (SSP), were created. Because of this multiplicity, publishers started to create competition between SSPs. In this setting, there are two successive auctions: a second price auction in each SSP and a secondary, first price auction, called header bidding auction, between SSPs.In this paper, we consider an SSP competing with other SSPs for ad spaces. The SSP acts as an intermediary between an advertiser wanting to buy ad spaces and a web publisher wanting to sell its ad spaces, and needs to define a bidding strategy to be able to deliver to the advertisers as many ads as possible while spending as little as possible. The revenue optimization of this SSP can be written as a contextual bandit problem, where the context consists of the information available about the ad opportunity, such as properties of the internet user or of the ad placement.Using classical multi-armed bandit strategies (such as the original versions of UCB and EXP3) is inefficient in this setting and yields a low convergence speed, as the arms are very correlated. In this paper we design and experiment a version of the Thompson Sampling algorithm that easily takes this correlation into account. We combine this bayesian algorithm with a particle filter, which permits to handle non-stationarity by sequentially estimating the distribution of the highest bid to beat in order to win an auction. We apply this methodology on two real auction datasets, and show that it significantly outperforms more classical approaches.The strategy defined in this paper is being developed to be deployed on thousands of publishers worldwide.
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
Venue: arXiv cs.CV 2018
Year: 2018

The cardiothoracic ratio (CTR), a clinical metric of heart size in chest X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is time-consuming and can be affected by human subjectivity, making it desirable to design computer-aided systems that assist clinicians in the diagnosis process. Automatic CTR estimation through chest organ segmentation, however, requires large amounts of pixel-level annotated data, which is often unavailable. To alleviate this problem, we propose an unsupervised domain adaptation framework based on adversarial networks. The framework learns domain invariant feature representations from openly available data sources to produce accurate chest organ segmentation for unlabeled datasets. Specifically, we propose a model that enforces our intuition that prediction masks should be domain independent. Hence, we introduce a discriminator that distinguishes segmentation predictions from ground truth masks. We evaluate our system's prediction based on the assessment of radiologists and demonstrate the clinical practicability for the diagnosis of cardiomegaly. We finally illustrate on the JSRT dataset that the semi-supervised performance of our model is also very promising.
An Adaptive Learning Method of Restricted Boltzmann Machine by Neuron Generation and Annihilation Algorithm
Venue: arXiv cs.NE 2018
Year: 2018

Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based model of artificial neural network for unsupervised learning. Recently, RBM is well known to be a pre-training method of Deep Learning. In addition to visible and hidden neurons, the structure of RBM has a number of parameters such as the weights between neurons and the coefficients for them. Therefore, we may meet some difficulties to determine an optimal network structure to analyze big data. In order to evade the problem, we investigated the variance of parameters to find an optimal structure during learning. For the reason, we should check the variance of parameters to cause the fluctuation for energy function in RBM model. In this paper, we propose the adaptive learning method of RBM that can discover an optimal number of hidden neurons according to the training situation by applying the neuron generation and annihilation algorithm. In this method, a new hidden neuron is generated if the energy function is not still converged and the variance of the parameters is large. Moreover, the inactivated hidden neuron will be annihilated if the neuron does not affect the learning situation. The experimental results for some benchmark data sets were discussed in this paper.
Efficient Reassembling of Three-Regular Planar Graphs
Venue: arXiv cs.DS 2018
Year: 2018

A reassembling of a simple graph G = (V,E) is an abstraction of a problem arising in earlier studies of network analysis. There are several equivalent definitions of graph reassembling; in this report we use a definition which makes it closest to the notion of graph carving. A reassembling is a rooted binary tree whose nodes are subsets of V and whose leaf nodes are singleton sets, with each of the latter containing a distinct vertex of G. The parent of two nodes in the reassembling is the union of the two children's vertex sets. The root node of the reassembling is the full set V. The edge-boundary degree of a node in the reassembling is the number of edges in G that connect vertices in the node's set to vertices not in the node's set. A reassembling's alpha-measure is the largest edge-boundary degree of any node in the reassembling. A reassembling of G is alpha-optimal if its alpha-measure is the minimum among all alpha-measures of G's reassemblings. The problem of finding an alpha-optimal reassembling of a simple graph in general was already shown to be NP-hard. In this report we present an algorithm which, given a 3-regular plane graph G = (V,E) as input, returns a reassembling of G with an alpha-measure independent of n (number of vertices in G) and upper-bounded by 2k, where k is the edge-outerplanarity of G. (Edge-outerplanarity is distinct but closely related to the usual notion of outerplanarity; as with outerplanarity, for a fixed edge-outerplanarity k, the number n of vertices can be arbitrarily large.) Our algorithm runs in time linear in n. Moreover, we construct a class of $3$-regular plane graphs for which this alpha-measure is optimal, by proving that 2k is the lower bound on the alpha-measure of any reassembling of a graph in that class.
The Recommendation System to SNS Community for Tourists by Using Altruistic Behaviors
Venue: arXiv cs.MA 2018
Year: 2018

We have already developed the recommendation system of sightseeing information on SNS by using smartphone based user participatory sensing system. The system can post the attractive information for tourists to the specified Facebook page by our developed smartphone application. The users in Facebook, who are interested in sightseeing, can come flocking through information space from far and near. However, the activities in the community on SNS are only supported by the specified people called a hub. We proposed the method of vitalization of tourist behaviors to give a stimulus to the people. We developed the simulation system for multi agent system with altruistic behaviors inspired by the Army Ants. The army ant takes feeding action with altruistic behaviors to suppress selfish behavior to a common object used by a plurality of users in common. In this paper, we introduced the altruism behavior determined by some simulation to vitalize the SNS community. The efficiency of the revitalization process of the community was investigated by some experimental simulation results.
A Recommendation System of Grants to Acquire External Funds
Venue: arXiv cs.IR 2018
Year: 2018

The recommendation system of the competitive grants to university researchers by using the Grants-in-Aid for Scientific Research (KAKEN) keywords has been developed. The system can determine the recommendation order of researchers to each grant by the using the association rules between KAKEN application and various information from the web site of the corresponding grant. However, our developed previous system has some fatal errors in the retrieval algorithm. We modify the algorithm and extend the retrieval data for web mining. If the grant information is not enough to determine the relation, the system investigates the past KAKEN records in the database for the researcher who acquired the past grant. Moreover, the system retrieves the papers of the researchers to search their interests. As a result, the agreement degree of the researcher's interest to the grant increases. This paper discusses some simulation results.
Multiuser One-Bit Massive MIMO Precoding Under MPSK Signaling
Venue: arXiv cs.IT 2018
Year: 2018

Most recently, there has been a flurry of research activities on studying how massive MIMO precoding should be designed when the digital-to-analog conversion at the transmitter side is operated by cheap one-bit digital-to-analog converters (DACs). Such research is motivated by the desire to substantially cut down the hardware cost and power consumption of the radio-frequency chain, which is unaffordable in massive MIMO if high-resolution DACs are still used. One-bit MIMO precoding design problems are much harder to solve than their high-resolution DAC counterparts. In our previous work, we developed a minimum symbol-error probability (SEP) design for one-bit precoding under the multiuser MISO downlink scenario and under quadrature amplitude modulation signaling. Leveraging on the previous work, this work shows how the minimum SEP design is applied to M-ary phase shift keying (MPSK) signaling. Simulation results show that our minimum SEP design delivers significantly better bit-error rate (BER) performance than the other designs for higher-order PSK such as 8-PSK and 16-PSK. As a minor, but useful, side contribution, we also tackle an MPSK SEP characterization problem which was only intuitively treated in the prior arts.
DXRAM's Fault-Tolerance Mechanisms Meet High Speed I/O Devices
Venue: arXiv cs.DC 2018
Year: 2018

In-memory key-value stores provide consistent low-latency access to all objects which is important for interactive large-scale applications like social media networks or online graph analytics and also opens up new application areas. But, when storing the data in RAM on thousands of servers one has to consider server failures. Only a few in-memory key-value stores provide automatic online recovery of failed servers. The most prominent example of these systems is RAMCloud. Another system with sophisticated fault-tolerance mechanisms is DXRAM which is optimized for small data objects. In this report, we detail the remote replication process which is based on logs, investigate selection strategies for the reorganization of these logs and evaluate the reorganization performance for sequential, random, zipf and hot-and-cold distributions in DXRAM. This is also the first time DXRAM's backup system is evaluated with high speed I/O devices, specifically with 56 GBit/s InfiniBand interconnect and PCI-e SSDs. Furthermore, we discuss the copyset replica distribution to reduce the probability for data loss and the adaptations to the original approach for DXRAM.
SiL: An Approach for Adjusting Applications to Heterogeneous Systems Under Perturbations
Venue: arXiv cs.DC 2018
Year: 2018

Scientific applications consist of large and computationally-intensive loops. Dynamic loop scheduling (DLS) techniques are used to load balance the execution of such applications. Load imbalance can be caused by variations in loop iteration execution times due to problem, algorithmic, or systemic characteristics (also, perturbations). The following question motivates this work: "Given an application, a high-performance computing (HPC) system, and both their characteristics and interplay, which DLS technique will achieve improved performance under unpredictable perturbations?" Existing work only considers perturbations caused by variations in the HPC system delivered computational speeds. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. Simulator in the loop (SiL) is introduced, herein, as a new control-theoretic inspired approach to dynamically select DLS techniques that improve the performance of applications on heterogeneous HPC systems under perturbations. The present work examines the performance of six applications on a heterogeneous system under all above system perturbations. The SiL proof of concept is evaluated using simulation. The performance results confirm the initial hypothesis that no single DLS technique can deliver best performance in all scenarios, while the SiL-based DLS selection delivered improved application performance in most experiments.
Multi-D Kneser-Ney Smoothing Preserving the Original Marginal Distributions
Venue: arXiv cs.CL 2018
Year: 2018

Smoothing is an essential tool in many NLP tasks, therefore numerous techniques have been developed for this purpose in the past. One of the most widely used smoothing methods are the Kneser-Ney smoothing (KNS) and its variants, including the Modified Kneser-Ney smoothing (MKNS), which are widely considered to be among the best smoothing methods available. Although when creating the original KNS the intention of the authors was to develop such a smoothing method that preserves the marginal distributions of the original model, this property was not maintained when developing the MKNS. In this article I would like to overcome this and propose such a refined version of the MKNS that preserves these marginal distributions while keeping the advantages of both previous versions. Beside its advantageous properties, this novel smoothing method is shown to achieve about the same results as the MKNS in a standard language modelling task.
Successive Cancellation List Decoding of BMERA Codes with Application to Higher-Order Modulation
Venue: arXiv cs.IT 2018
Year: 2018

BMERA or convolutional polar codes are an extension of polar codes with a provably better error exponent than polar codes. A successive cancellation (SC) decoding algorithm for BMERA codes similar to SC polar decoders is introduced. A pseudocode description of the SC decoder that can be extended to SC list (SCL) decoding is provided. Simulation results with and without outer CRC codes under SC and SCL decoding are presented for QAM modulation over the AWGN channel to compare the performance of polar and BMERA codes. BMERA codes outperform polar codes by more than 0.5 dB under SCL decoding without outer CRC codes.
Theoretical Model of Computation and Algorithms for FPGA-based Hardware Accelerators
Venue: arXiv cs.DS 2018
Year: 2018

While FPGAs have been used extensively as hardware accelerators in industrial computation, no theoretical model of computation has been devised for the study of FPGA-based accelerators. In this paper, we present a theoretical model of computation on a system with conventional CPU and an FPGA, based on word-RAM. We show several algorithms in this model which are asymptotically faster than their word-RAM counterparts. Specifically, we show an algorithm for sorting, evaluation of associative operation and general techniques for speeding up some recursive algorithms and some dynamic programs. We also derive lower bounds on the running times needed to solve some problems.
Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images
Venue: arXiv cs.CV 2018
Year: 2018

Optical coherence tomography (OCT) can provide high-resolution cross-sectional images for analyzing superficial plaques in coronary arteries. Commonly, plaque characterization using intra-coronary OCT images is performed manually by expert observers. This manual analysis is time consuming and its accuracy heavily relies on the experience of human observers. Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images. Several processing steps, including feature extraction, informative feature selection, and final pixel classification, are commonly used in these traditional methods. Therefore, the final classification accuracy can be jeopardized by error or inaccuracy within each of these steps. In this study, we proposed a convolutional neural network (CNN) based method to automatically characterize plaques in OCT images. Unlike traditional methods, our method uses the image as a direct input and performs classification as a single-step process. The experiments on 269 OCT images showed that the average prediction accuracy of CNN-based method was 0.866, which indicated a great promise for clinical translation.
Interpretable Patient Mortality Prediction with Multi-value Rule Sets
Venue: arXiv cs.AI 2018
Year: 2018

We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting patient mortality. Compared to rule sets built from single-valued rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-valued rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating a MRS model and propose an efficient inference method for learning a maximum \emph{a posteriori}, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency. Experiments show that our model was able to achieve better performance than baseline method including the current system used by the hospital.
TrialChain: A Blockchain-Based Platform to Validate Data Integrity in Large, Biomedical Research Studies
Venue: arXiv cs.DC 2018
Year: 2018

The governance of data used for biomedical research and clinical trials is an important requirement for generating accurate results. To improve the visibility of data quality and analysis, we developed TrialChain, a blockchain-based platform that can be used to validate data integrity from large, biomedical research studies. We implemented a private blockchain using the MultiChain platform and integrated it with a data science platform deployed within a large research center. An administrative web application was built with Python to manage the platform, which was built with a microservice architecture using Docker. The TrialChain platform was integrated during data acquisition into our existing data science platform. Using NiFi, data were hashed and logged within the local blockchain infrastructure. To provide public validation, the local blockchain state was periodically synchronized to the public Ethereum network. The use of a combined private/public blockchain platform allows for both public validation of results while maintaining additional security and lower cost for blockchain transactions. Original data and modifications due to downstream analysis can be logged within TrialChain and data assets or results can be rapidly validated when needed using API calls to the platform. The TrialChain platform provides a data governance solution to audit the acquisition and analysis of biomedical research data. The platform provides cryptographic assurance of data authenticity and can also be used to document data analysis.
Event Detection and Retrieval on Social Media
Venue: arXiv cs.SI 2018
Year: 2018

In the recent years, we have witnessed the rapid adoption of social media platforms, such as Twitter, Facebook and YouTube, and their use as part of the everyday life of billions of people worldwide. Given the habit of people to use these platforms to share thoughts, daily activities and experiences it is not surprising that the amount of user generated content has reached unprecedented levels, with a substantial part of that content being related to real-world events, i.e. actions or occurrences taking place at a certain time and location. Given the key role of events in our life, the task of annotating and organizing social media content around them is of crucial importance for ensuring real-time and future access to multimedia content about an event of interest. In this chapter, we present several research efforts from recent years that tackle two main problems: a) event detection and b) event-based media retrieval and summarization. Given archived collections or live streams of social media items, the purpose of event detection methods is to identify previously unknown events in the form of sets of items that describe them. In general, the events could be of any type, but there are also approaches aiming at events of specific type. Given a target event the goal of event summarization is first to identify relevant content and then to represent it in a concise way, selecting the most appealing and representative content.
sAVSS: Scalable Asynchronous Verifiable Secret Sharing in BFT Protocols
Venue: arXiv cs.CR 2018
Year: 2018

This paper introduces a new way to incorporate verifiable secret sharing (VSS) schemes into Byzantine Fault Tolerance (BFT) protocols. This technique extends the threshold guarantee of classical Byzantine Fault Tolerant algorithms to include privacy as well. This provides applications with a powerful primitive: a threshold trusted third party, which simplifies many difficult problems such as a fair exchange. In order to incorporate VSS into BFT, we introduced sAVSS, a framework that transforms any VSS scheme into an asynchronous VSS scheme with constant overhead. By incorporating Kate et al.'s scheme into our framework, we obtain an asynchronous VSS that has constant overhead on each replica -- the first of its kind. We show that a key-value store built using BFT replication and sAVSS supports writing secret-shared values with about a 30% - 50% throughput overhead with less than 35 millisecond request latencies.